Method and device for controlling a technical system by means of control models

ABSTRACT

In order to control a technical system by means of control model a data container is received, in which data container a control model having a training structure and model type information are encoded over all the model types. One of multiple model-typespecific execution modules is selected for the technical system as a function of the model type information. Furthermore, operating data channels of the technical system are assigned input channels of the control model as a function of the model type information. Operating data of the technical system are acquired via a respective operating data channel and are transferred to the control model via an input channel assigned to this operating data channel. The control model is executed by means of the selected execution module, wherein control data are derived from the transferred operating data according to the training structure and are output to control the technical system.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to PCT Application No.PCT/EP2017/052133, having a filing date of Feb. 1, 2017 based off ofGerman application No. DE102016203855.9, having a filing date of Mar. 9,2016, the entire contents of both of which are hereby incorporated byreference.

FIELD OF TECHNOLOGY

In the control of complex technical systems, such as for example gasturbines, wind turbines or manufacturing installations, it is generallydesirable to optimize the system behavior with regard to prescribedcriteria. To this end, modern control systems often use machine learningtechniques. A neural network may thus be trained as a control model, forexample, to optimize a technical system with regard to a multiplicity ofcriteria.

BACKGROUND

Meanwhile, the control of relatively large installations in particularplaces high requirements in terms of safety and flexibility on thecontrol systems that are used, which in many cases have to be subjectedto an extensive certification process. However, this generally makes theuse of learning-based control systems more difficult, as their internalresponse relationships are often difficult to comprehend externally andmay change depending on a training state. A multiplicity of controlmodels having different implementation requirements also exist.Accordingly, using learning-based control models, especially in the caseof relatively large technical systems, often proves cumbersome.

SUMMARY

An aspect relates to specifying a method and a device for controlling atechnical system, which method and device allow more flexible use oflearning-based control models.

To control a technical system by means of a trained and/or trainablecontrol model, a data container is received in which a control modelhaving a training structure and model type information are encoded forall model types. The training structure may in this case in particularconcern a structure that is trained, trainable, teachable and/or trainedduring training and/or a training state of the control model. Trainingis understood in this case in particular to mean that computation ofinput parameters of the control model is optimized during a trainingphase with respect to one or more target variables depending onprescribable criteria. Such training may be performed for example bytraining a neural network, by regression of an analytical or statisticalmodel and/or by another type of parameter fitting. According toembodiments of the invention, one of a plurality of model type-specificexecution modules is selected for the technical system depending on themodel type information. Furthermore, operating data channels of thetechnical system are assigned to input channels of the control modeldepending on the model type information. Operating data of the technicalsystem are acquired via a respective operating data channel and aretransmitted to the control model via an input channel assigned to thisoperating data channel. The control model is executed by way of theselected execution module, wherein control data, which are output inorder to control the technical system, are derived from the transmittedoperating data according to the training structure. Forecast data and/ormonitoring data relevant to control may also be output as control data.

In order to perform the method according to embodiments of theinvention, a control device, a computer program product (non-transitorycomputer readable storage medium having instructions, which whenexecuted by a processor, perform actions) and a computer-readablestorage medium are provided.

One advantage of embodiments of the invention is observed in that, byway of the model type-specific execution modules, control structures ofthe technical system are largely able to be decoupled from specificrequirements of different control model types. This allows easier andmore flexible implementation of completely different control models. Inparticular, different control models are able to be executedautomatically on the same technical system, and the same control modelis able to be executed automatically on different technical systems. Byway of the model type-specific assignment of operating data channels toinput channels, the control model is able to be driven appropriatelywith operating data.

The control model may preferably comprise a neural network, adata-driven regressor, a support vector machine and/or a decision tree.The above implementations may in each case be provided with a trainingstructure and identified by specific model type information.

According to one advantageous embodiment of the invention, the controlmodel may be present in the data container in encrypted form and be atleast partly decrypted by the technical system. The control model maythus be protected against unauthorized access during transmission andstorage.

Preferably, the control model may be encoded and/or encrypted in such away that derivation of a model structure of the control model on thebasis of parts of the control model, which parts are decoded and/ordecrypted in order to execute the control model, is prevented or madedifficult. The model structure may concern for example the trainingstructure and/or a specific layer, node, network or weighting structureof a neural network. The control model may in this case be encryptedsuch that only parts of the control model that are relevant to executionare able to be decrypted by the technical system, but a specific modelstructure remains largely concealed. Thus, in the case of a neuralnetwork, only one executable calculation routine may be encoded, fromwhich a specific network structure is able to be derived only withdifficulty. The control model may thus be executed as a black box, as itwere. By way of such encoding or encryption, know-how contained in themodel structure is able to be protected.

Furthermore, the control model in the data container may be providedwith a digital signature that is checked by the technical system, forexample. The control model may then be executed depending on the resultof the check. Integrity of the control model is thus able to be ensured.In particular, creation of, training of and/or changes to the controlmodel are able to be attributed unambiguously to a responsibleauthority.

Furthermore, the data container may comprise training informationregarding training of the control model. The execution of the controlmodel and/or the selection of the execution module may then take placedepending on the training information. The training information may inparticular concern a previous or upcoming training process for thecontrol model.

According to one advantageous embodiment of the invention, the operatingdata channels and the input channels may each be assigned a data type, aphysical dimension, a range of values and/or an additional condition. Inthe assignment of a respective operating data channel to a respectiveinput channel, it may be checked whether the assigned data types,physical dimensions, ranges of values and/or additional conditions arecompatible. By way of example, meters, seconds, grams or combinationsthereof may be assigned as physical dimensions. In many cases, it isthus able to be ensured that the control model is driven with correctoperating data. As an alternative or in addition, the acquired operatingdata may be checked with regard to data type, physical dimension, rangeof values and/or additional conditions, in order thus to ensure correctdriving of the control model during the execution.

Furthermore, the control data may be checked with regard to their rangeof values, a change in value and/or an additional condition. Since, inthe case of trained control models, the dependencies of the output dataon input data are not generally explicitly known, and it is often notpossible to rule out model errors, incorrect control is often able to beavoided by checking prescribed additional conditions.

According to one advantageous development of embodiments of theinvention, a plurality of runtime environment-specific adapters may eachbe assigned to a runtime environment. The adapters in this case serve toadapt execution modules to the assigned runtime environment.Furthermore, environment information regarding a runtime environment ofthe technical system may be acquired and, depending on the acquiredenvironment information, an adapter assigned to the runtime environmentof the technical system may be selected. The selected execution modulemay then be coupled to the runtime environment via the selected adapter.Execution modules and control models may thus be created and implementedlargely independently of the respective runtime environment.Accordingly, adapters may be developed and implemented largelyindependently of the type of the control model. Control models andruntime environment may thus be decoupled, as it were, as a result ofwhich a development and/or implementation process is often simplifiedconsiderably.

Furthermore, the selected adapter may provide capability informationregarding capabilities of the runtime environment of the technicalsystem. Depending on the capability information, compatibility of thecontrol model with the runtime environment may be checked and thecontrol model may be executed depending thereon. Automaticimplementation of the control model is thus able to be simplified.

BRIEF DESCRIPTION

Some of the embodiments will be described in detail, with reference tothe following figures, wherein like designations denote like members,wherein:

FIG. 1 shows a data container having an encoded control model;

FIG. 2 shows a technical system having a control device;

FIG. 3 shows an illustration of derivation of control data fromoperating data by way of a control model; and

FIG. 4 shows an illustration of an interaction of control models withruntime environments.

DETAILED DESCRIPTION

FIG. 1 schematically illustrates a data container DC according toembodiments of the invention having an encoded, trained and/or trainablecontrol model SM. The control model SM serves to simulate a physical,regulation-based and/or stochastic dynamic system or anotherinterdependency of a technical system or of part thereof. The controlmodel SM may comprise a neural network, a data-driven regressor, asupport vector machine, a decision tree and/or another analytical modelor a combination thereof. The control model SM is encoded in the datacontainer DC for all model types, for example in what is calledPMML-format (PMML: Predictive Model Markup Language) or in a proprietaryformat.

In the present exemplary embodiment, the control model SM isadditionally encrypted for data security purposes during transmissionand storage. Furthermore, the control model SM has a training structureTSR. The training structure TSR comprises a teachable structure,preferably in a pre-trained training state. In the case of a neuralnetwork, the training structure TSR may for example comprise a networkstructure of neurons and weighting of connections between neurons. Inthe case of data-driven regressors, the training structure TSR maycontain coefficients of the regressor model. The training structure TSRmay relate both to previous and to future training of the control modelSM.

The data container DC furthermore contains technical metadata TM andmodel metadata MM.

The technical metadata TM comprise, in the present exemplary embodiment,model type information MTI, training information TI and input/outputcontract data IOC. Furthermore, the technical metadata TM containcontext information, such as for example a creation time of the controlmodel SM, information regarding intended target systems and/orinformation regarding requirements of the control model SM in terms of aruntime environment, for example with regard to real-time capabilities,parallelizability, computing resources and/or compatibility withdifferent execution modules.

The model type information MTI is encoded for all model types andspecifies a type of the control model SM. In this case, it may bespecified for example whether the control model SM is based on a neuralnetwork, on a data-driven regressor, on a support vector machine, on adecision tree and/or on a combination thereof. Furthermore, inputvariables and/or output variables of the control model SM and otherspecific requirements, capabilities and/or properties of the controlmodel SM may be specified.

The training information TI describes a previous and/or upcomingtraining process and/or a training state of the control model SM.

The input/output contract data IOC specify what is called aninput/output contract, which sets additional conditions in terms of abehavior of the control model SM. By way of the input/output contractdata IOC, prescribed additional conditions, such as for example rangesof values, changes in values, a rate of changes in values and/or datatypes of input data and/or output data of the control model SM maypreferably be specified in a format for all model types. Theinput/output contract data IOC may advantageously be specified in auser-readable format in order thus to ensure, in a manner able to bechecked, a desired behavior of the control model SM whose trainingstructure TSR is not generally user-readable.

The model metadata MINI contain, in the present exemplary embodiment,one or more digital signatures SIG, for example of individuals and/orauthorities that have created, trained and/or changed the control modelSM.

Furthermore, the model metadata MINI may comprise version information,rights information, and information regarding a source and/or regardinga target system of the control model SM. Furthermore, specificationsregarding a period of validity, regarding required data processingresources and/or regarding admissible or possible areas of use, forexample for monitoring, for forecasting and/or for control, may becontained in the model metadata MM.

FIG. 2 shows a schematic illustration of a technical system TS having acontrol device CTL according to embodiments of the invention for thetechnical system TS. The technical system TS may be for example a powerplant, a production installation, a gas turbine, etc.

The technical system TS has a runtime environment RE for data processingpurposes and for controlling the technical system TS. Such a runtimeenvironment, RE here, may comprise a combination of operating system,cloud/cluster middleware and/or data processing environment. Examples ofthese are a Linux cluster with Hadoop/HIVE framework, a cluster streamprocessing environment or a multicore stream processing environment.

The control device CTL, for example a control system for a gas turbine,contains one or more processors PROC for executing all of the methodsteps of the control device CTL, and a model execution system MES. Thelatter is implemented in the control device CTL in the present exemplaryembodiment, but may, as an alternative or in addition, be implemented atleast partly externally, for example in a cloud. The model executionsystem IVIES may be used as an abstraction layer between control modelsand the runtime environment RE. The model execution system MES comprisesa plurality of execution modules EM1, EM2 and EM3, and a plurality ofadapters AD1 and AD2.

The execution modules EM1, EM2 and EM3 serve to execute, install,initialize and/or evaluate trained and/or trainable control models onthe or for the technical system. The execution modules EM1, EM2 and EM3are in each case specific to a control model type. Such executionmodules are often also referred to as interpreters.

The adapters AD1 and AD2 serve to adapt execution modules, here EM1, EM2and EM3, to different runtime environments. The adapters AD1 and AD2 arein each case specific to a runtime environment.

To select a runtime environment-specific adapter, the model executionsystem MES acquires environment information EI that describes theruntime environment RE from the runtime environment RE present in thetechnical system TS. Depending on the acquired environment informationEI, the model execution system IVIES selects one of the adapters AD1,AD2, which is specific to and suitable for the runtime environment, hereRE, described by the environment information EI. In the presentexemplary embodiment, the adapter AD2 proves to be suitable for thepresent runtime environment RE and is therefore selected and coupled tothe runtime environment RE.

The selected adapter AD2 then provides capability information CIregarding specific capabilities of the runtime environment RE, on thebasis of which information compatibility of control models with theruntime environment RE is able to be checked by the model executionsystem MES.

To control the technical system TS, the model execution system IVIESreceives various data containers DC1 and DC2, which are each configuredas described in FIG. 1. The data containers DC1 and DC2 each contain aspecific control model SM1 and SM2, respectively. The data containersDC1 and DC2 are preferably transmitted to the technical system TS asspecific messages.

The control models SM1 and SM2 are each configured as described inconnection with FIG. 1, and serve to simulate various physical,regulation-based, stochastic and/or other interdependencies of thetechnical system TS or of part thereof. The control models SM1 and SM2are in each case preferably specific to particular parts of thetechnical system, particular regulation tasks, particular control tasksand/or particular simulation types. The control models SM1 and SM2 arein each case encoded for all model types.

By way of the data containers DC1 and DC2, in each case model typeinformation MTI1 and MTI2, respectively, for the control model SM1 andSM2, respectively, is transmitted to the model execution system MES.MTI1 and MTI2 each specify a model type of the control model SM1 andSM2, respectively, and may in each case be configured as described inconnection with FIG. 1.

Following reception of the data containers DC1 and DC2, the modelexecution system IVIES unpacks these data containers and checks each oftheir digital signatures. If the result of the check is negative,further processing of the control model SM1 or SM2 in question issuppressed. Furthermore, the model execution system IVIES checks, for arespective control model SM1 or SM2, on the basis of their technicalmetadata and on the basis of the capability information CI, whether andto what extent the respective control model SM1 or SM2 is compatiblewith the runtime environment RE. Depending on this, further processingof the respective control model SM1 or SM2 takes place.

Furthermore, the model execution system IVIES decrypts the encryptedcontrol models SM1 and SM2. In this case, preferably only parts of therespective control model that are relevant to execution are decrypted,so that the control model is able to be executed, but a model structureof the control model is not able to be derived with reasonable effort.

Furthermore, the model execution system IVIES selects, for each controlmodel SM1 and SM2, an execution module that is in each case specificthereto on the basis of the model type information MTI1 and MTI2,respectively, and possibly on the basis of other technical metadata. Inthe present exemplary embodiment, the execution module EM1 is selectedand assigned for the control model SM1, and the execution module EM2 isselected and assigned for the control model SM2. The model executionsystem IVIES then couples the selected execution modules EM1 and EM2 tothe runtime environment RE by way of the selective adapter AD2.

To process operating data of the technical system TS, the modelexecution system IVIES executes the control models SM1 and/or SM2 on theruntime environment RE, wherein the model execution system MES delegatesthe execution of a respective control model SM1 or SM2 to therespectively assigned execution module EM1 or EM2. During the executionof a respective control model SM1 or SM2, by means of the input/outputcontract data of the respective control model SM1 or SM2, compliancewith the input/output contract specified thereby is monitored andensured.

FIG. 3 illustrates derivation of control data CD from operating data BDof the technical system TS by way of a control model SM that is executedby an execution module EM on the technical system TS by way of a coupledadapter AD. For the sake of clarity, the same technical system TS isillustrated schematically on both sides of FIG. 3. The technical systemTS, the control model SM, the execution module EM and the adapter AD arepreferably configured as described in connection with FIGS. 1 and 2.

The control model SM is implemented by way of a neural network NN thathas a training structure TSR.

The technical system TS has sensors S for acquiring the operating dataBD of the technical system TS. The operating data BD may be for examplephysical, regulation-based and/or design-related operating variables,properties, prescribed values, state data, system data, control data,sensor data and/or measured values of the technical system TS. Inparticular, the operating data BD may also comprise data not originatingfrom the sensors S.

The operating data BD are acquired via specific operating data channelsBDC of the technical system TS. The operating data channels BDC may inthis case be specific to a data type, to a physical dimension, to asource, to a function and/or to other properties of the operating dataBD.

The control model SM has various input channels IC, which are assignedto various input parameters or input data of the control model SM. Theinput channels IC may be specific to a parameter type, to a physicaldimension, to a source, to a function and/or to other properties of theinput parameters or input data.

An assignment IMAP takes place between the input channels IC and theoperating data channels BDC, in which assignment in each case one of theoperating data channels BDC is assigned to a respective input channel ICon the basis of the model type information MTI and possibly on the basisof the input/output contract data IOC. The assignment IMAP is carriedout by the model execution system MES, preferably by way of the selectedexecution module EM.

On the basis of the data types, physical dimensions, ranges of valuesand/or additional conditions each respectively assigned to the operatingdata channels BDC and input channels IC, the model execution system MESchecks whether the assigned data types, physical dimensions, ranges ofvalues and additional conditions of the input channels IC are compatiblewith those of the assigned operating data channels BDC. If this is notthe case, execution of the control model SM is suppressed.

The operating data BD acquired via the operating data channels BDC aresupplied to the control model SM as input data via the assigned inputchannels IC. The model execution system IVIES executes the control modelSM by way of the selected execution module EM, wherein control data CDare derived from the transmitted operating data BD according to thetraining structure TSR. The control data CD are output as output data ofthe control model SM. The control data CD serve in this case to controlthe technical system TS and may in particular also be forecast data andmonitoring data relevant to control.

The control data CD are output via specific output channels OC of thecontrol model SM that are assigned to various output parameters of thecontrol model SM. The output channels OC may in each case be specific toa parameter type, a use, a purpose and/or a control function of thecontrol data CD output thereby.

The model execution system IVIES executes an assignment OMAP of arespective output channel OC to one of a plurality of control channelsCDC of the technical system, preferably by way of the selected executionmodule EM. The assignment in this case takes place depending on themodel type information MTI and possibly depending on the input/outputcontract data IOC. In the assignment OMAP, it is checked whether theassigned data types, physical dimensions, ranges of values etc. of thecontrol channels CDC and of the output channels OC are compatible withone another. If not, the execution of the control model SM issuppressed.

During the execution of the control model SM, on the basis of theinput/output contract data IOC, compliance with the relevantinput/output contract is monitored and ensured by the model executionsystem MES.

FIG. 4 illustrates interaction of various control models SM1, . . . ,SMM with various runtime environments RE1, . . . , REN.

The runtime environments RE1, . . . , REN are in each case coupled asdescribed above to a model execution system MES via one of a pluralityof runtime environment-specific adapters AD1, . . . , ADN, respectively.Furthermore, the control models SM1, . . . , SMM are in each casecoupled as described above to the model execution system IVIES via oneof a plurality of model type-specific execution modules EM1, . . . ,EMM, respectively.

Arrival of operating data in one of the runtime environments RE1, . . ., REN, respectively, or acquisition of these operating data by one ofthese runtime environments initiates data-driven processing of theoperating data through delegation DBD of the processing of the acquiredruntime environment RE1, . . . , REN, respectively, via a runtimeenvironment-specific adapter AD1, . . . , ADN, respectively, and a modeltype-specific execution module EM1, . . . , EMM, respectively, to thespecific control model SM1, . . . , SMM, respectively. The delegationDBD is in this case performed by the model execution system MES.

Outputting of the control data derived by a respective control modelSM1, . . . , SMM, respectively, initiates delegation DCD, performed bythe model execution system MES, of the control process of the technicalsystem. The delegation DCD takes place from a respective control modelSM1, . . . , SMM, respectively, via the respective assigned modeltype-specific execution module EM1, . . . , EMM, respectively, and therespective runtime environment-specific adapter AD1, . . . , ADN,respectively, to the assigned runtime environment RE1, . . . , REN,respectively, which controls the technical system TS by means of thecontrol data CD.

Although the present invention has been disclosed in the form ofpreferred embodiments and variations thereon, it will be understood thatnumerous additional modifications and variations could be made theretowithout departing from the scope of the invention.

For the sake of clarity, it is to be understood that the use of “a” or“an” throughout this application does not exclude a plurality, and“comprising” does not exclude other steps or elements.

1. A method for controlling a technical system by means of a trainedand/or trainable control model, the method comprising: receiving, by aprocessor, a data container in which a control model having a trainingstructure and model type information are encoded for all model types: a)selecting, by the processor, one of a plurality of model type-specificexecution modules for the technical system depending on the model typeinformation; b) assigning, by the processor, operating data channels ofthe technical system to input channels of the control model depending onthe model type information; c) acquiring, by the processor, operatingdata of the technical system via a respective operating data channeltransmitting the operating data to the control model via an inputchannel assigned to the operating data channel; d) executing, by theprocessor, the control model by way of the selected execution module,wherein control data are derived from the transmitted operating dataaccording to the training structure; and e) outputting, by theprocessor, the control data to control the technical system.
 2. Themethod as claimed in claim 1, wherein the control model comprises aneural network, a data-driven regressor, a support vector machine and ora decision tree.
 3. The method as claimed in claim 1, wherein thecontrol model is present in the data container in encrypted form and isat least partly decrypted by the technical system.
 4. The method asclaimed in claim 1, wherein the control model is encoded and orencrypted in such a way that derivation of a model structure of thecontrol model on the basis of parts of the control model, which partsare decoded and or decrypted in order to execute the control model, isprevented or made difficult.
 5. The method as claimed in claim 1,wherein the control model in the data container is provided with adigital signature, the digital signature is checked, and the controlmodel is executed depending on the result of the check.
 6. The method asclaimed in claim 1, wherein the data container comprises traininginformation regarding training of the control model, and in that theexecution of the control model and/or the selection of the executionmodule takes place depending on the training information.
 7. The methodas claimed in claim 1, wherein the operating data channels and the inputchannels are each assigned a data type, a physical dimension, a range ofvalues and/or an additional condition, and, in the assignment of arespective operating data channel to a respective input channel, it ischecked whether the assigned data types, physical dimensions, ranges ofvalues and or additional conditions are compatible.
 8. The method asclaimed in claim 1, wherein the control data are checked with regard totheir range of values, a change in value and or an additional condition.9. The method as claimed in claim 1, wherein a plurality of runtimeenvironment-specific adapters are each assigned to a runtime environmentin order to adapt execution modules to the assigned runtime environment,in that environment information regarding a runtime environment of thetechnical system is acquired, in that, depending on the acquiredenvironment information, an adapter assigned to the runtime environmentof the technical system is selected, and in that the selected executionmodule is coupled to the runtime environment via the selected adapter.10. The method as claimed in claim 9, wherein the selected adapterprovides capability information regarding capabilities of the runtimeenvironment of the technical system, and depending on the capabilityinformation, compatibility of the control model with the runtimeenvironment is checked and the control model is executed dependingthereon.
 11. The method as claimed in claim 1, wherein the control modelis regularly retrained or continuously trained by means of thetransmitted operating data.
 12. A device for controlling a technicalsystem by means of a trained and/or trainable control model, configuredto execute a method as claimed in claim
 1. 13. A computer programproduct, comprising a computer readable hardware storage device havingcomputer readable program code stored therein, said program codeexecutable by a processor of a computer system to implement a method asclaimed in claim
 1. 14. A computer-readable storage medium having acomputer program product as claimed in claim 13.